English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Pretrained contextualized language models such as BERT and T5 haveestablished a new state-of-the-art for ad-hoc search. However, it is not yetwell-understood why these methods are so effective, what makes some variantsmore effective than others, and what pitfalls they may have. We present a newcomprehensive framework for Analyzing the Behavior of Neural IR ModeLs(ABNIRML), which includes new types of diagnostic probes that allow us to testseveral characteristics -- such as writing styles, factuality, sensitivity toparaphrasing and word order -- that are not addressed by previous techniques.To demonstrate the value of the framework, we conduct an extensive empiricalstudy that yields insights into the factors that contribute to the neuralmodel's gains, and identify potential unintended biases the models exhibit.Some of our results confirm conventional wisdom, like that recent neuralranking models rely less on exact term overlap with the query, and insteadleverage richer linguistic information, evidenced by their higher sensitivityto word and sentence order. Other results are more surprising, such as thatsome models (e.g., T5 and ColBERT) are biased towards factually correct (ratherthan simply relevant) texts. Further, some characteristics vary even for thesame base language model, and other characteristics can appear due to randomvariations during model training.

ABNIRML: Analyzing the Behavior of Neural IR Models